Overview

Dataset statistics

Number of variables27
Number of observations378
Missing cells802
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.9 KiB
Average record size in memory203.0 B

Variable types

Numeric20
Categorical4
Boolean3

Alerts

Nbr_Is_Dest_Cnt has constant value ""Constant
Nbr_Never_Dest has constant value ""Constant
Index is highly overall correlated with NodeHigh correlation
Nbr_Count is highly overall correlated with Pct_of_All_Nbrs and 1 other fieldsHigh correlation
AODV_Msg_Nbr_Cnt is highly overall correlated with RREQs_From_Nbr and 8 other fieldsHigh correlation
RREQs_Sent_To_Nbr is highly overall correlated with NodeHigh correlation
RREQs_From_Nbr is highly overall correlated with AODV_Msg_Nbr_Cnt and 8 other fieldsHigh correlation
Nbr_Is_Orig_Cnt is highly overall correlated with AODV_Msg_Nbr_Cnt and 3 other fieldsHigh correlation
All_RREPs_Rcvd_This_Node is highly overall correlated with NodeHigh correlation
RREPs_From_Nbr is highly overall correlated with AODV_Msg_Nbr_Cnt and 8 other fieldsHigh correlation
RREP_Resp_Pct is highly overall correlated with AODV_Msg_Nbr_Cnt and 9 other fieldsHigh correlation
Pct_Of_All_RREPs is highly overall correlated with AODV_Msg_Nbr_Cnt and 8 other fieldsHigh correlation
Hop_Cnt_Over_1_Cnt is highly overall correlated with AODV_Msg_Nbr_Cnt and 8 other fieldsHigh correlation
Hop_Cnt_Over_1_Pct is highly overall correlated with Black_Hole_NodeHigh correlation
High_Dest_Seq_Num_Inc_Cnt is highly overall correlated with High_Dest_Seq_Num_Inc_Pct and 1 other fieldsHigh correlation
Avg_Resp_Dly is highly overall correlated with Avg_Resp_Dly_Per_HopHigh correlation
Avg_Resp_Dly_Per_Hop is highly overall correlated with Avg_Resp_DlyHigh correlation
RERRs_From_Nbr is highly overall correlated with AODV_Msg_Nbr_Cnt and 8 other fieldsHigh correlation
Pct_of_All_Nbrs is highly overall correlated with Nbr_Count and 1 other fieldsHigh correlation
RREP_To_Nbrs_Ratio is highly overall correlated with AODV_Msg_Nbr_Cnt and 8 other fieldsHigh correlation
Node is highly overall correlated with Index and 4 other fieldsHigh correlation
Nbr_Node is highly overall correlated with High_Dest_Seq_Num_Inc_Pct and 1 other fieldsHigh correlation
Nbr_Never_Orig is highly overall correlated with AODV_Msg_Nbr_Cnt and 4 other fieldsHigh correlation
High_Dest_Seq_Num_Inc_Pct is highly overall correlated with RREPs_From_Nbr and 6 other fieldsHigh correlation
Black_Hole_Node is highly overall correlated with RREPs_From_Nbr and 7 other fieldsHigh correlation
High_Dest_Seq_Num_Inc_Pct is highly imbalanced (77.1%)Imbalance
Black_Hole_Node is highly imbalanced (74.7%)Imbalance
Hop_Cnt_Over_1_Pct has 158 (41.8%) missing valuesMissing
High_Dest_Seq_Num_Inc_Pct has 158 (41.8%) missing valuesMissing
Avg_Resp_Dly has 164 (43.4%) missing valuesMissing
Avg_Resp_Dly_Per_Hop has 164 (43.4%) missing valuesMissing
RERRs_From_Nbr_Pct has 158 (41.8%) missing valuesMissing
Index is uniformly distributedUniform
Index has unique valuesUnique
AODV_Msg_Nbr_Cnt has 138 (36.5%) zerosZeros
RREQs_From_Nbr has 149 (39.4%) zerosZeros
Nbr_Is_Orig_Cnt has 279 (73.8%) zerosZeros
RREPs_From_Nbr has 158 (41.8%) zerosZeros
RREP_Resp_Pct has 158 (41.8%) zerosZeros
Pct_Of_All_RREPs has 158 (41.8%) zerosZeros
Hop_Cnt_Over_1_Cnt has 182 (48.1%) zerosZeros
Hop_Cnt_Over_1_Pct has 24 (6.3%) zerosZeros
High_Dest_Seq_Num_Inc_Cnt has 360 (95.2%) zerosZeros
RERRs_From_Nbr has 169 (44.7%) zerosZeros
RERRs_From_Nbr_Pct has 21 (5.6%) zerosZeros
RREP_To_Nbrs_Ratio has 158 (41.8%) zerosZeros

Reproduction

Analysis started2023-04-11 15:08:28.559870
Analysis finished2023-04-11 15:09:32.061253
Duration1 minute and 3.5 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Index
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct378
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.5
Minimum1
Maximum378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:32.158294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.85
Q195.25
median189.5
Q3283.75
95-th percentile359.15
Maximum378
Range377
Interquartile range (IQR)188.5

Descriptive statistics

Standard deviation109.26344
Coefficient of variation (CV)0.57658809
Kurtosis-1.2
Mean189.5
Median Absolute Deviation (MAD)94.5
Skewness0
Sum71631
Variance11938.5
MonotonicityStrictly increasing
2023-04-12T01:09:32.296262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
249 1
 
0.3%
258 1
 
0.3%
257 1
 
0.3%
256 1
 
0.3%
255 1
 
0.3%
254 1
 
0.3%
253 1
 
0.3%
252 1
 
0.3%
251 1
 
0.3%
Other values (368) 368
97.4%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
378 1
0.3%
377 1
0.3%
376 1
0.3%
375 1
0.3%
374 1
0.3%
373 1
0.3%
372 1
0.3%
371 1
0.3%
370 1
0.3%
369 1
0.3%

Node
Categorical

Distinct10
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
10.1.1.48
48 
10.1.1.45
45 
10.1.1.42
43 
10.1.1.43
42 
10.1.1.40
37 
Other values (5)
163 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters3402
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10.1.1.40
2nd row10.1.1.40
3rd row10.1.1.40
4th row10.1.1.40
5th row10.1.1.40

Common Values

ValueCountFrequency (%)
10.1.1.48 48
12.7%
10.1.1.45 45
11.9%
10.1.1.42 43
11.4%
10.1.1.43 42
11.1%
10.1.1.40 37
9.8%
10.1.1.49 35
9.3%
10.1.1.47 35
9.3%
10.1.1.46 35
9.3%
10.1.1.44 34
9.0%
10.1.1.41 24
6.3%

Length

2023-04-12T01:09:32.435682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-12T01:09:32.599599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
10.1.1.48 48
12.7%
10.1.1.45 45
11.9%
10.1.1.42 43
11.4%
10.1.1.43 42
11.1%
10.1.1.40 37
9.8%
10.1.1.49 35
9.3%
10.1.1.47 35
9.3%
10.1.1.46 35
9.3%
10.1.1.44 34
9.0%
10.1.1.41 24
6.3%

Most occurring characters

ValueCountFrequency (%)
1 1158
34.0%
. 1134
33.3%
0 415
 
12.2%
4 412
 
12.1%
8 48
 
1.4%
5 45
 
1.3%
2 43
 
1.3%
3 42
 
1.2%
9 35
 
1.0%
7 35
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2268
66.7%
Other Punctuation 1134
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1158
51.1%
0 415
 
18.3%
4 412
 
18.2%
8 48
 
2.1%
5 45
 
2.0%
2 43
 
1.9%
3 42
 
1.9%
9 35
 
1.5%
7 35
 
1.5%
6 35
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 1134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3402
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1158
34.0%
. 1134
33.3%
0 415
 
12.2%
4 412
 
12.1%
8 48
 
1.4%
5 45
 
1.3%
2 43
 
1.3%
3 42
 
1.2%
9 35
 
1.0%
7 35
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3402
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1158
34.0%
. 1134
33.3%
0 415
 
12.2%
4 412
 
12.1%
8 48
 
1.4%
5 45
 
1.3%
2 43
 
1.3%
3 42
 
1.2%
9 35
 
1.0%
7 35
 
1.0%

Nbr_Node
Categorical

Distinct50
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
10.1.1.31
 
10
10.1.1.36
 
10
10.1.1.30
 
10
10.1.1.20
 
10
10.1.1.2
 
10
Other values (45)
328 

Length

Max length9
Median length9
Mean length8.7910053
Min length8

Characters and Unicode

Total characters3323
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10.1.1.37
2nd row10.1.1.3
3rd row10.1.1.5
4th row10.1.1.12
5th row10.1.1.41

Common Values

ValueCountFrequency (%)
10.1.1.31 10
 
2.6%
10.1.1.36 10
 
2.6%
10.1.1.30 10
 
2.6%
10.1.1.20 10
 
2.6%
10.1.1.2 10
 
2.6%
10.1.1.1 10
 
2.6%
10.1.1.27 10
 
2.6%
10.1.1.37 10
 
2.6%
10.1.1.49 10
 
2.6%
10.1.1.3 10
 
2.6%
Other values (40) 278
73.5%

Length

2023-04-12T01:09:32.796630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10.1.1.31 10
 
2.6%
10.1.1.27 10
 
2.6%
10.1.1.5 10
 
2.6%
10.1.1.3 10
 
2.6%
10.1.1.49 10
 
2.6%
10.1.1.37 10
 
2.6%
10.1.1.34 10
 
2.6%
10.1.1.1 10
 
2.6%
10.1.1.2 10
 
2.6%
10.1.1.20 10
 
2.6%
Other values (40) 278
73.5%

Most occurring characters

ValueCountFrequency (%)
1 1236
37.2%
. 1134
34.1%
0 415
 
12.5%
3 126
 
3.8%
4 107
 
3.2%
2 104
 
3.1%
7 45
 
1.4%
5 44
 
1.3%
6 40
 
1.2%
8 37
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2189
65.9%
Other Punctuation 1134
34.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1236
56.5%
0 415
 
19.0%
3 126
 
5.8%
4 107
 
4.9%
2 104
 
4.8%
7 45
 
2.1%
5 44
 
2.0%
6 40
 
1.8%
8 37
 
1.7%
9 35
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 1134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3323
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1236
37.2%
. 1134
34.1%
0 415
 
12.5%
3 126
 
3.8%
4 107
 
3.2%
2 104
 
3.1%
7 45
 
1.4%
5 44
 
1.3%
6 40
 
1.2%
8 37
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1236
37.2%
. 1134
34.1%
0 415
 
12.5%
3 126
 
3.8%
4 107
 
3.2%
2 104
 
3.1%
7 45
 
1.4%
5 44
 
1.3%
6 40
 
1.2%
8 37
 
1.1%

Nbr_Count
Real number (ℝ)

Distinct8
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.936508
Minimum24
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:32.897205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile24
Q135
median37
Q343
95-th percentile48
Maximum48
Range24
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.2338335
Coefficient of variation (CV)0.16010253
Kurtosis-0.096067988
Mean38.936508
Median Absolute Deviation (MAD)5
Skewness-0.4499659
Sum14718
Variance38.86068
MonotonicityNot monotonic
2023-04-12T01:09:32.996820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
35 105
27.8%
48 48
12.7%
45 45
11.9%
43 43
11.4%
42 42
 
11.1%
37 37
 
9.8%
34 34
 
9.0%
24 24
 
6.3%
ValueCountFrequency (%)
24 24
 
6.3%
34 34
 
9.0%
35 105
27.8%
37 37
 
9.8%
42 42
 
11.1%
43 43
11.4%
45 45
11.9%
48 48
12.7%
ValueCountFrequency (%)
48 48
12.7%
45 45
11.9%
43 43
11.4%
42 42
 
11.1%
37 37
 
9.8%
35 105
27.8%
34 34
 
9.0%
24 24
 
6.3%

Hello_Cnt
Real number (ℝ)

Distinct73
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.074074
Minimum0
Maximum160
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:33.128309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q111
median19
Q334
95-th percentile61.15
Maximum160
Range160
Interquartile range (IQR)23

Descriptive statistics

Standard deviation25.049337
Coefficient of variation (CV)0.96069902
Kurtosis10.67998
Mean26.074074
Median Absolute Deviation (MAD)10
Skewness2.8671842
Sum9856
Variance627.4693
MonotonicityNot monotonic
2023-04-12T01:09:33.277128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 22
 
5.8%
18 15
 
4.0%
11 15
 
4.0%
7 14
 
3.7%
4 14
 
3.7%
14 13
 
3.4%
9 13
 
3.4%
28 13
 
3.4%
10 10
 
2.6%
12 10
 
2.6%
Other values (63) 239
63.2%
ValueCountFrequency (%)
0 1
 
0.3%
1 5
 
1.3%
2 6
1.6%
3 2
 
0.5%
4 14
3.7%
5 8
2.1%
6 6
1.6%
7 14
3.7%
8 9
2.4%
9 13
3.4%
ValueCountFrequency (%)
160 1
0.3%
148 1
0.3%
145 2
0.5%
144 2
0.5%
140 1
0.3%
136 2
0.5%
133 1
0.3%
84 1
0.3%
77 2
0.5%
75 1
0.3%

AODV_Msg_Nbr_Cnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct139
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.724868
Minimum0
Maximum444
Zeros138
Zeros (%)36.5%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:33.426588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24
Q385.75
95-th percentile224.2
Maximum444
Range444
Interquartile range (IQR)85.75

Descriptive statistics

Standard deviation84.003085
Coefficient of variation (CV)1.4304517
Kurtosis4.7084506
Mean58.724868
Median Absolute Deviation (MAD)24
Skewness2.0596519
Sum22198
Variance7056.5183
MonotonicityNot monotonic
2023-04-12T01:09:33.561718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 138
36.5%
13 7
 
1.9%
16 6
 
1.6%
24 5
 
1.3%
83 5
 
1.3%
41 5
 
1.3%
34 4
 
1.1%
1 4
 
1.1%
36 4
 
1.1%
11 4
 
1.1%
Other values (129) 196
51.9%
ValueCountFrequency (%)
0 138
36.5%
1 4
 
1.1%
3 1
 
0.3%
4 1
 
0.3%
5 1
 
0.3%
6 2
 
0.5%
7 3
 
0.8%
8 2
 
0.5%
9 3
 
0.8%
10 1
 
0.3%
ValueCountFrequency (%)
444 1
0.3%
433 1
0.3%
414 1
0.3%
403 1
0.3%
385 1
0.3%
356 1
0.3%
343 1
0.3%
341 1
0.3%
337 2
0.5%
326 1
0.3%

RREQs_Sent_To_Nbr
Real number (ℝ)

Distinct9
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean220.83333
Minimum111
Maximum272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:33.692128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile111
Q1200
median214
Q3259
95-th percentile272
Maximum272
Range161
Interquartile range (IQR)59

Descriptive statistics

Standard deviation41.323865
Coefficient of variation (CV)0.18712694
Kurtosis0.7439764
Mean220.83333
Median Absolute Deviation (MAD)21
Skewness-0.818677
Sum83475
Variance1707.6618
MonotonicityNot monotonic
2023-04-12T01:09:33.791532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
200 70
18.5%
272 48
12.7%
193 45
11.9%
203 43
11.4%
214 42
11.1%
261 37
9.8%
254 35
9.3%
259 34
9.0%
111 24
 
6.3%
ValueCountFrequency (%)
111 24
 
6.3%
193 45
11.9%
200 70
18.5%
203 43
11.4%
214 42
11.1%
254 35
9.3%
259 34
9.0%
261 37
9.8%
272 48
12.7%
ValueCountFrequency (%)
272 48
12.7%
261 37
9.8%
259 34
9.0%
254 35
9.3%
214 42
11.1%
203 43
11.4%
200 70
18.5%
193 45
11.9%
111 24
 
6.3%

RREQs_From_Nbr
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.939153
Minimum0
Maximum241
Zeros149
Zeros (%)39.4%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:33.923979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q338
95-th percentile128.6
Maximum241
Range241
Interquartile range (IQR)38

Descriptive statistics

Standard deviation43.042376
Coefficient of variation (CV)1.5405755
Kurtosis6.0168701
Mean27.939153
Median Absolute Deviation (MAD)10
Skewness2.3421805
Sum10561
Variance1852.6462
MonotonicityNot monotonic
2023-04-12T01:09:34.083551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 149
39.4%
10 8
 
2.1%
38 8
 
2.1%
21 7
 
1.9%
13 7
 
1.9%
36 6
 
1.6%
8 6
 
1.6%
18 6
 
1.6%
17 6
 
1.6%
5 5
 
1.3%
Other values (91) 170
45.0%
ValueCountFrequency (%)
0 149
39.4%
1 5
 
1.3%
2 2
 
0.5%
3 2
 
0.5%
4 3
 
0.8%
5 5
 
1.3%
6 4
 
1.1%
7 1
 
0.3%
8 6
 
1.6%
9 5
 
1.3%
ValueCountFrequency (%)
241 1
0.3%
239 1
0.3%
216 1
0.3%
200 1
0.3%
185 1
0.3%
180 1
0.3%
174 1
0.3%
173 1
0.3%
170 1
0.3%
166 1
0.3%

Nbr_Is_Orig_Cnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0925926
Minimum0
Maximum65
Zeros279
Zeros (%)73.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:34.217994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile14.15
Maximum65
Range65
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.0285057
Coefficient of variation (CV)3.3587549
Kurtosis27.291837
Mean2.0925926
Median Absolute Deviation (MAD)0
Skewness4.8297628
Sum791
Variance49.399892
MonotonicityNot monotonic
2023-04-12T01:09:34.324008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 279
73.8%
1 40
 
10.6%
2 11
 
2.9%
4 8
 
2.1%
5 8
 
2.1%
3 5
 
1.3%
29 3
 
0.8%
8 2
 
0.5%
28 2
 
0.5%
15 2
 
0.5%
Other values (16) 18
 
4.8%
ValueCountFrequency (%)
0 279
73.8%
1 40
 
10.6%
2 11
 
2.9%
3 5
 
1.3%
4 8
 
2.1%
5 8
 
2.1%
6 1
 
0.3%
8 2
 
0.5%
10 2
 
0.5%
12 1
 
0.3%
ValueCountFrequency (%)
65 1
 
0.3%
43 1
 
0.3%
39 1
 
0.3%
34 1
 
0.3%
33 1
 
0.3%
31 2
0.5%
30 1
 
0.3%
29 3
0.8%
28 2
0.5%
25 1
 
0.3%
Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
True
279 
False
99 
ValueCountFrequency (%)
True 279
73.8%
False 99
 
26.2%
2023-04-12T01:09:34.465388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Nbr_Is_Dest_Cnt
Categorical

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
378 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters378
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 378
100.0%

Length

2023-04-12T01:09:34.584894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-12T01:09:34.704068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0 378
100.0%

Most occurring characters

ValueCountFrequency (%)
0 378
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 378
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 378
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 378
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 378
100.0%
Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
True
378 
ValueCountFrequency (%)
True 378
100.0%
2023-04-12T01:09:34.805164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

All_RREPs_Rcvd_This_Node
Real number (ℝ)

Distinct9
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean638.19577
Minimum106
Maximum961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:34.885205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile106
Q1430
median733
Q3833
95-th percentile961
Maximum961
Range855
Interquartile range (IQR)403

Descriptive statistics

Standard deviation287.48189
Coefficient of variation (CV)0.45046034
Kurtosis-0.73441404
Mean638.19577
Median Absolute Deviation (MAD)180
Skewness-0.74461705
Sum241238
Variance82645.834
MonotonicityNot monotonic
2023-04-12T01:09:34.984428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
833 70
18.5%
961 48
12.7%
666 45
11.9%
913 43
11.4%
106 42
11.1%
733 37
9.8%
430 35
9.3%
518 34
9.0%
139 24
 
6.3%
ValueCountFrequency (%)
106 42
11.1%
139 24
 
6.3%
430 35
9.3%
518 34
9.0%
666 45
11.9%
733 37
9.8%
833 70
18.5%
913 43
11.4%
961 48
12.7%
ValueCountFrequency (%)
961 48
12.7%
913 43
11.4%
833 70
18.5%
733 37
9.8%
666 45
11.9%
518 34
9.0%
430 35
9.3%
139 24
 
6.3%
106 42
11.1%

RREPs_From_Nbr
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.402116
Minimum0
Maximum272
Zeros158
Zeros (%)41.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:35.103552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q323.75
95-th percentile115
Maximum272
Range272
Interquartile range (IQR)23.75

Descriptive statistics

Standard deviation50.102888
Coefficient of variation (CV)2.053219
Kurtosis11.415264
Mean24.402116
Median Absolute Deviation (MAD)4
Skewness3.2984036
Sum9224
Variance2510.2994
MonotonicityNot monotonic
2023-04-12T01:09:35.248182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 158
41.8%
2 12
 
3.2%
1 9
 
2.4%
3 8
 
2.1%
4 8
 
2.1%
5 8
 
2.1%
11 8
 
2.1%
22 7
 
1.9%
15 6
 
1.6%
12 6
 
1.6%
Other values (67) 148
39.2%
ValueCountFrequency (%)
0 158
41.8%
1 9
 
2.4%
2 12
 
3.2%
3 8
 
2.1%
4 8
 
2.1%
5 8
 
2.1%
6 6
 
1.6%
7 5
 
1.3%
8 4
 
1.1%
9 3
 
0.8%
ValueCountFrequency (%)
272 2
0.5%
261 2
0.5%
259 2
0.5%
254 2
0.5%
214 2
0.5%
203 2
0.5%
200 2
0.5%
193 1
0.3%
151 1
0.3%
132 1
0.3%

RREP_Resp_Pct
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct145
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.108995
Minimum0
Maximum100
Zeros158
Zeros (%)41.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:35.379856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.55
Q311.5
95-th percentile55.8085
Maximum100
Range100
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation21.982118
Coefficient of variation (CV)1.9787675
Kurtosis8.9791469
Mean11.108995
Median Absolute Deviation (MAD)1.55
Skewness3.0019878
Sum4199.2
Variance483.21351
MonotonicityNot monotonic
2023-04-12T01:09:35.535002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 158
41.8%
100 16
 
4.2%
0.93 4
 
1.1%
23 4
 
1.1%
6.9 3
 
0.8%
1.55 3
 
0.8%
1.47 3
 
0.8%
8.43 3
 
0.8%
8.81 3
 
0.8%
1.92 3
 
0.8%
Other values (135) 178
47.1%
ValueCountFrequency (%)
0 158
41.8%
0.38 1
 
0.3%
0.39 3
 
0.8%
0.47 1
 
0.3%
0.52 1
 
0.3%
0.74 1
 
0.3%
0.77 1
 
0.3%
0.9 3
 
0.8%
0.93 4
 
1.1%
0.99 1
 
0.3%
ValueCountFrequency (%)
100 16
4.2%
65.02 1
 
0.3%
57.5 2
 
0.5%
55.51 1
 
0.3%
50.74 1
 
0.3%
48.5 2
 
0.5%
46.31 1
 
0.3%
45.96 1
 
0.3%
42.91 1
 
0.3%
39.41 1
 
0.3%

Pct_Of_All_RREPs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct150
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3795357
Minimum0
Maximum100
Zeros158
Zeros (%)41.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:35.676351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.68
Q34.2375
95-th percentile16.7545
Maximum100
Range100
Interquartile range (IQR)4.2375

Descriptive statistics

Standard deviation11.078122
Coefficient of variation (CV)2.5295198
Kurtosis38.16954
Mean4.3795357
Median Absolute Deviation (MAD)0.68
Skewness5.5765482
Sum1655.4645
Variance122.72479
MonotonicityNot monotonic
2023-04-12T01:09:35.808181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 158
41.8%
3 5
 
1.3%
5.52 4
 
1.1%
1.89 4
 
1.1%
5.04 3
 
0.8%
1.44 3
 
0.8%
0.45 3
 
0.8%
1.16 3
 
0.8%
0.6 3
 
0.8%
2.46 3
 
0.8%
Other values (140) 189
50.0%
ValueCountFrequency (%)
0 158
41.8%
0.14 1
 
0.3%
0.15 1
 
0.3%
0.19 2
 
0.5%
0.21 1
 
0.3%
0.22 1
 
0.3%
0.23 1
 
0.3%
0.24 2
 
0.5%
0.3 2
 
0.5%
0.31 2
 
0.5%
ValueCountFrequency (%)
100 2
0.5%
79.85611511 1
0.3%
59.06976744 2
0.5%
50 2
0.5%
35.60709413 2
0.5%
28.97897898 1
0.3%
28.30385016 2
0.5%
24.53 1
0.3%
24.46 1
0.3%
24.00960384 2
0.5%

Hop_Cnt_Over_1_Cnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.277778
Minimum0
Maximum136
Zeros182
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:35.959857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q315
95-th percentile59.75
Maximum136
Range136
Interquartile range (IQR)15

Descriptive statistics

Standard deviation20.163643
Coefficient of variation (CV)1.7879092
Kurtosis8.3814885
Mean11.277778
Median Absolute Deviation (MAD)1
Skewness2.6746408
Sum4263
Variance406.5725
MonotonicityNot monotonic
2023-04-12T01:09:36.105904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 182
48.1%
1 16
 
4.2%
4 12
 
3.2%
2 11
 
2.9%
10 10
 
2.6%
11 9
 
2.4%
3 9
 
2.4%
7 8
 
2.1%
18 8
 
2.1%
14 7
 
1.9%
Other values (46) 106
28.0%
ValueCountFrequency (%)
0 182
48.1%
1 16
 
4.2%
2 11
 
2.9%
3 9
 
2.4%
4 12
 
3.2%
5 6
 
1.6%
6 3
 
0.8%
7 8
 
2.1%
8 1
 
0.3%
9 3
 
0.8%
ValueCountFrequency (%)
136 1
0.3%
110 1
0.3%
99 1
0.3%
94 1
0.3%
89 1
0.3%
83 2
0.5%
82 1
0.3%
76 1
0.3%
73 1
0.3%
69 2
0.5%

Hop_Cnt_Over_1_Pct
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct96
Distinct (%)43.6%
Missing158
Missing (%)41.8%
Infinite0
Infinite (%)0.0%
Mean67.847636
Minimum0
Maximum100
Zeros24
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:36.260566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median78.02
Q392.215
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)42.215

Descriptive statistics

Standard deviation31.466314
Coefficient of variation (CV)0.46377908
Kurtosis-0.14939868
Mean67.847636
Median Absolute Deviation (MAD)18.5
Skewness-0.96380571
Sum14926.48
Variance990.12893
MonotonicityNot monotonic
2023-04-12T01:09:36.402666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 37
 
9.8%
0 24
 
6.3%
50 10
 
2.6%
66.67 8
 
2.1%
40 4
 
1.1%
96.15 4
 
1.1%
83.33 4
 
1.1%
33.33 3
 
0.8%
90.91 3
 
0.8%
68.57 3
 
0.8%
Other values (86) 120
31.7%
(Missing) 158
41.8%
ValueCountFrequency (%)
0 24
6.3%
20 1
 
0.3%
25 2
 
0.5%
28.57 3
 
0.8%
30 1
 
0.3%
31.75 1
 
0.3%
33.33 3
 
0.8%
36.36 1
 
0.3%
37.25 1
 
0.3%
38.89 1
 
0.3%
ValueCountFrequency (%)
100 37
9.8%
97.87 1
 
0.3%
97.56 1
 
0.3%
97.06 1
 
0.3%
96.15 4
 
1.1%
95.83 1
 
0.3%
95.45 2
 
0.5%
95.35 1
 
0.3%
94.44 1
 
0.3%
94.12 3
 
0.8%

High_Dest_Seq_Num_Inc_Cnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6084656
Minimum0
Maximum272
Zeros360
Zeros (%)95.2%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:36.534494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum272
Range272
Interquartile range (IQR)0

Descriptive statistics

Standard deviation46.526227
Coefficient of variation (CV)4.8422119
Kurtosis21.417242
Mean9.6084656
Median Absolute Deviation (MAD)0
Skewness4.7753264
Sum3632
Variance2164.6898
MonotonicityNot monotonic
2023-04-12T01:09:36.633151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 360
95.2%
1 2
 
0.5%
200 2
 
0.5%
203 2
 
0.5%
214 2
 
0.5%
254 2
 
0.5%
259 2
 
0.5%
261 2
 
0.5%
272 2
 
0.5%
111 1
 
0.3%
ValueCountFrequency (%)
0 360
95.2%
1 2
 
0.5%
111 1
 
0.3%
193 1
 
0.3%
200 2
 
0.5%
203 2
 
0.5%
214 2
 
0.5%
254 2
 
0.5%
259 2
 
0.5%
261 2
 
0.5%
ValueCountFrequency (%)
272 2
0.5%
261 2
0.5%
259 2
0.5%
254 2
0.5%
214 2
0.5%
203 2
0.5%
200 2
0.5%
193 1
0.3%
111 1
0.3%
1 2
0.5%

High_Dest_Seq_Num_Inc_Pct
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)1.8%
Missing158
Missing (%)41.8%
Memory size5.9 KiB
0.0
202 
100.0
 
16
12.5
 
1
6.67
 
1

Length

Max length5
Median length3
Mean length3.1545455
Min length3

Characters and Unicode

Total characters694
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.9%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 202
53.4%
100.0 16
 
4.2%
12.5 1
 
0.3%
6.67 1
 
0.3%
(Missing) 158
41.8%

Length

2023-04-12T01:09:36.764915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-12T01:09:36.925425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 202
91.8%
100.0 16
 
7.3%
12.5 1
 
0.5%
6.67 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 452
65.1%
. 220
31.7%
1 17
 
2.4%
6 2
 
0.3%
2 1
 
0.1%
5 1
 
0.1%
7 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 474
68.3%
Other Punctuation 220
31.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 452
95.4%
1 17
 
3.6%
6 2
 
0.4%
2 1
 
0.2%
5 1
 
0.2%
7 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 220
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 694
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 452
65.1%
. 220
31.7%
1 17
 
2.4%
6 2
 
0.3%
2 1
 
0.1%
5 1
 
0.1%
7 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 452
65.1%
. 220
31.7%
1 17
 
2.4%
6 2
 
0.3%
2 1
 
0.1%
5 1
 
0.1%
7 1
 
0.1%

Avg_Resp_Dly
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct191
Distinct (%)89.3%
Missing164
Missing (%)43.4%
Infinite0
Infinite (%)0.0%
Mean2.4795477
Minimum0.005808
Maximum5.246532
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:37.061822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.005808
5-th percentile0.36039559
Q11.7648783
median2.5696854
Q33.2871231
95-th percentile4.2729725
Maximum5.246532
Range5.240724
Interquartile range (IQR)1.5222447

Descriptive statistics

Standard deviation1.1315999
Coefficient of variation (CV)0.45637353
Kurtosis-0.35231036
Mean2.4795477
Median Absolute Deviation (MAD)0.77695363
Skewness-0.1961299
Sum530.6232
Variance1.2805184
MonotonicityNot monotonic
2023-04-12T01:09:37.201133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.275154 2
 
0.5%
2.222242478 2
 
0.5%
2.966004 2
 
0.5%
2.609925333 2
 
0.5%
2.979843229 2
 
0.5%
3.578571295 2
 
0.5%
2.353775609 2
 
0.5%
4.57753624 2
 
0.5%
1.341238909 2
 
0.5%
3.534384072 2
 
0.5%
Other values (181) 194
51.3%
(Missing) 164
43.4%
ValueCountFrequency (%)
0.005808 1
0.3%
0.0136725 1
0.3%
0.017664 1
0.3%
0.02700775 2
0.5%
0.027943667 1
0.3%
0.12149575 1
0.3%
0.26499 1
0.3%
0.326765 1
0.3%
0.350953 1
0.3%
0.358436667 1
0.3%
ValueCountFrequency (%)
5.246532 1
0.3%
4.807704 1
0.3%
4.775200333 1
0.3%
4.7432248 1
0.3%
4.720705769 1
0.3%
4.57753624 2
0.5%
4.472448234 1
0.3%
4.3191378 1
0.3%
4.294276059 1
0.3%
4.291812412 1
0.3%

Avg_Resp_Dly_Per_Hop
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct191
Distinct (%)89.3%
Missing164
Missing (%)43.4%
Infinite0
Infinite (%)0.0%
Mean0.58583649
Minimum0.002904
Maximum2.8807045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:37.354520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.002904
5-th percentile0.099038868
Q10.40121027
median0.57582248
Q30.78867219
95-th percentile0.99858994
Maximum2.8807045
Range2.8778005
Interquartile range (IQR)0.38746193

Descriptive statistics

Standard deviation0.32586224
Coefficient of variation (CV)0.55623411
Kurtosis12.170481
Mean0.58583649
Median Absolute Deviation (MAD)0.18841238
Skewness1.9470294
Sum125.36901
Variance0.1061862
MonotonicityNot monotonic
2023-04-12T01:09:37.488891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.480730592 2
 
0.5%
0.812302725 2
 
0.5%
0.415334718 2
 
0.5%
1.0294065 2
 
0.5%
0.006001396 2
 
0.5%
0.916393714 2
 
0.5%
0.370851458 2
 
0.5%
0.479314045 2
 
0.5%
0.796855799 2
 
0.5%
0.765961518 2
 
0.5%
Other values (181) 194
51.3%
(Missing) 164
43.4%
ValueCountFrequency (%)
0.002904 1
0.3%
0.005588733 1
0.3%
0.005888 1
0.3%
0.006001396 2
0.5%
0.010383 1
0.3%
0.032873875 1
0.3%
0.043850926 1
0.3%
0.070563832 1
0.3%
0.088126872 1
0.3%
0.095771444 1
0.3%
ValueCountFrequency (%)
2.8807045 1
0.3%
2.085936833 1
0.3%
1.168351417 2
0.5%
1.163595773 1
0.3%
1.138943288 1
0.3%
1.111085741 1
0.3%
1.037995143 1
0.3%
1.0294065 2
0.5%
1.024248499 1
0.3%
0.984773792 1
0.3%

RERRs_From_Nbr
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.589947
Minimum0
Maximum86
Zeros169
Zeros (%)44.7%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:37.639934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q317
95-th percentile44.15
Maximum86
Range86
Interquartile range (IQR)17

Descriptive statistics

Standard deviation16.488517
Coefficient of variation (CV)1.5569971
Kurtosis4.9165007
Mean10.589947
Median Absolute Deviation (MAD)2
Skewness2.1229897
Sum4003
Variance271.8712
MonotonicityNot monotonic
2023-04-12T01:09:37.765920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 169
44.7%
3 19
 
5.0%
2 13
 
3.4%
4 12
 
3.2%
10 10
 
2.6%
5 9
 
2.4%
11 8
 
2.1%
1 8
 
2.1%
24 8
 
2.1%
7 7
 
1.9%
Other values (45) 115
30.4%
ValueCountFrequency (%)
0 169
44.7%
1 8
 
2.1%
2 13
 
3.4%
3 19
 
5.0%
4 12
 
3.2%
5 9
 
2.4%
6 2
 
0.5%
7 7
 
1.9%
8 3
 
0.8%
9 3
 
0.8%
ValueCountFrequency (%)
86 1
0.3%
83 2
0.5%
82 1
0.3%
79 1
0.3%
71 1
0.3%
65 2
0.5%
58 2
0.5%
57 1
0.3%
56 1
0.3%
55 1
0.3%

RERRs_From_Nbr_Pct
Real number (ℝ)

MISSING  ZEROS 

Distinct132
Distinct (%)60.0%
Missing158
Missing (%)41.8%
Infinite0
Infinite (%)0.0%
Mean100.72082
Minimum0
Maximum1400
Zeros21
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:37.963062image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q138.2325
median69.535
Q3111.2775
95-th percentile288.125
Maximum1400
Range1400
Interquartile range (IQR)73.045

Descriptive statistics

Standard deviation136.02558
Coefficient of variation (CV)1.350521
Kurtosis40.707299
Mean100.72082
Median Absolute Deviation (MAD)34.915
Skewness5.2634365
Sum22158.58
Variance18502.959
MonotonicityNot monotonic
2023-04-12T01:09:38.296851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
 
5.6%
100 11
 
2.9%
33.33 6
 
1.6%
150 5
 
1.3%
50 4
 
1.1%
300 4
 
1.1%
75 4
 
1.1%
66.67 3
 
0.8%
87.5 3
 
0.8%
57.14 3
 
0.8%
Other values (122) 156
41.3%
(Missing) 158
41.8%
ValueCountFrequency (%)
0 21
5.6%
10 1
 
0.3%
11.76 1
 
0.3%
12 1
 
0.3%
14.29 1
 
0.3%
16.67 1
 
0.3%
18.18 1
 
0.3%
21.21 1
 
0.3%
22.22 1
 
0.3%
24.74 2
 
0.5%
ValueCountFrequency (%)
1400 1
 
0.3%
650 1
 
0.3%
633.33 1
 
0.3%
600 1
 
0.3%
566.67 1
 
0.3%
550 1
 
0.3%
350 1
 
0.3%
300 4
1.1%
287.5 1
 
0.3%
283.33 1
 
0.3%

Pct_of_All_Nbrs
Real number (ℝ)

Distinct8
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6458466
Minimum2.08
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:38.441294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.08
5-th percentile2.08
Q12.33
median2.7
Q32.86
95-th percentile4.17
Maximum4.17
Range2.09
Interquartile range (IQR)0.53

Descriptive statistics

Standard deviation0.5004141
Coefficient of variation (CV)0.18913194
Kurtosis2.8653759
Mean2.6458466
Median Absolute Deviation (MAD)0.32
Skewness1.5441537
Sum1000.13
Variance0.25041427
MonotonicityNot monotonic
2023-04-12T01:09:38.542989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2.86 105
27.8%
2.08 48
12.7%
2.22 45
11.9%
2.33 43
11.4%
2.38 42
 
11.1%
2.7 37
 
9.8%
2.94 34
 
9.0%
4.17 24
 
6.3%
ValueCountFrequency (%)
2.08 48
12.7%
2.22 45
11.9%
2.33 43
11.4%
2.38 42
 
11.1%
2.7 37
 
9.8%
2.86 105
27.8%
2.94 34
 
9.0%
4.17 24
 
6.3%
ValueCountFrequency (%)
4.17 24
 
6.3%
2.94 34
 
9.0%
2.86 105
27.8%
2.7 37
 
9.8%
2.38 42
 
11.1%
2.33 43
11.4%
2.22 45
11.9%
2.08 48
12.7%

RREP_To_Nbrs_Ratio
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct155
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6524328
Minimum0
Maximum42.016807
Zeros158
Zeros (%)41.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2023-04-12T01:09:38.674245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.24395168
Q31.4685315
95-th percentile6.2679851
Maximum42.016807
Range42.016807
Interquartile range (IQR)1.4685315

Descriptive statistics

Standard deviation4.1984233
Coefficient of variation (CV)2.5407528
Kurtosis48.080481
Mean1.6524328
Median Absolute Deviation (MAD)0.24395168
Skewness6.0656556
Sum624.61961
Variance17.626758
MonotonicityNot monotonic
2023-04-12T01:09:38.805713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 158
41.8%
1.93006993 4
 
1.1%
0.794117647 4
 
1.1%
0.911111111 3
 
0.8%
0.201923077 3
 
0.8%
1.111111111 3
 
0.8%
0.202702703 3
 
0.8%
1.208633094 3
 
0.8%
0.172661871 3
 
0.8%
0.251851852 3
 
0.8%
Other values (145) 191
50.5%
ValueCountFrequency (%)
0 158
41.8%
0.051851852 1
 
0.3%
0.06462585 2
 
0.5%
0.067567568 1
 
0.3%
0.08041958 1
 
0.3%
0.083916084 2
 
0.5%
0.094420601 1
 
0.3%
0.100961538 1
 
0.3%
0.132653061 1
 
0.3%
0.135135135 2
 
0.5%
ValueCountFrequency (%)
42.01680672 2
0.5%
20.65376484 2
0.5%
19.15014751 1
0.3%
17.00680272 2
0.5%
13.60762027 2
0.5%
13.18781264 2
0.5%
13.05359413 1
0.3%
10.30672269 1
0.3%
9.54265756 2
0.5%
8.394966378 2
0.5%

Black_Hole_Node
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
False
362 
True
 
16
ValueCountFrequency (%)
False 362
95.8%
True 16
 
4.2%
2023-04-12T01:09:38.972375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Interactions

2023-04-12T01:09:27.787647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:30.985002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:33.818513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:36.470047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:39.518915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:42.184542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:45.296871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:48.089649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:51.033814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:55.018206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:59.074371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:02.524755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:05.313525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:08.329252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:10.972584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:13.672052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:16.214588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:18.962530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:21.546702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:25.173979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:27.925741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:31.135464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:33.940602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:36.631374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:39.655856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:42.352521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:45.435988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:48.237578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:51.231115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:55.191609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:59.387875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:02.669606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:05.466277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:08.476556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:11.109712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:13.806465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:16.371702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:19.105023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:21.713703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:25.316760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:28.046797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:31.280124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:34.074147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:36.772874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:39.790977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:42.493844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:45.569873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:48.365727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:51.434350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:55.327177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:59.593635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:02.807505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:05.599885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:08.603312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:11.245548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:13.929521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:16.496549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:19.220150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:21.859613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:25.441516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:28.188892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:31.438651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:34.215013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:36.934407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:39.943970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:42.655214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:45.735683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:48.526960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:51.631562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:55.519107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:59.806614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:02.976395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:05.753953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:08.744637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:11.392477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:14.069493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:16.644215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:19.363430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:22.009564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:25.583481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:28.315956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:31.573450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:34.337784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:37.090843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:40.070415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:42.789340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:45.863259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:48.672764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:51.873367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:55.666233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:59.991138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:03.117667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:05.888500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:08.866771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:11.521698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:14.189013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:16.774170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:19.489873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:22.130830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:25.707408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:28.459211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:31.727916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:34.482844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:37.264967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:40.215913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:42.961154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:46.009145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:48.834451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:52.065323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:55.823060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:00.205142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:03.272671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:06.051877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:09.017684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:11.672493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:14.330104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:16.926512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:19.631476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:23.016095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:25.853288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:28.587147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:31.872653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:34.614122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:37.408505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:40.343964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:43.106528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:46.134479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:48.971793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:52.242334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:55.956225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:00.366980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:03.404194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:06.190422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:09.144353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:11.794828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:14.457963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:17.046661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:19.759234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:23.170323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:25.981669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:28.717334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:32.012745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:34.734476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:37.593503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:40.481034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:43.257147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:46.277781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:49.126728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:52.403975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:56.091379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:00.535542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:03.545150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:06.337644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:09.277779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:11.944168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:14.587472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:17.203393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:19.891679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:23.329720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:26.115680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:28.848151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:32.148922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:34.862401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:37.742332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:40.597258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:43.424115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:46.407358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:49.261492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:52.543440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:56.225008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:00.683947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:03.681979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:06.475619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:09.403113image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:12.076542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:14.710014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:17.334944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:20.015198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:23.475062image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:26.242256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:28.982240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:32.287121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:35.009191image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:37.889491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:40.741485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:43.584733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:46.539950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:49.401744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:52.704318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:56.374396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:00.824340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:03.814144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:06.623075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:09.536143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:12.207248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:14.834197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:17.459247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:20.146102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:23.630650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:26.373043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:29.106531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:32.418050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:35.147878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:38.020194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:40.871179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:43.741202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:46.702275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:49.539360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:52.873198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:56.498563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:00.956435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:03.948662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:06.773355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:09.665055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:12.344764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:14.956316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:17.600356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:20.264825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:23.774787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:26.500504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:29.222719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:32.562762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:35.280772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:38.184019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:41.000273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:43.885600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:46.831273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:49.675085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:53.015335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:56.641087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:01.107740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:04.081499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:06.921404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:09.790358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:12.476335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:15.083277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:17.732747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:20.392766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:23.909558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:26.628838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:29.369143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:32.724149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:35.433498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:38.352290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:41.138644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:44.043383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:46.988121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:49.825684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:53.157112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:57.067033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:01.258295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:04.224332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:07.086936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:09.926203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:12.622851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:15.216557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:17.879994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:20.538948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:24.056546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:26.756157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:29.494808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:32.857599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:35.567268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:38.492957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:41.261827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:44.194512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:47.135650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:49.956993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:53.313595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:57.279594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:01.421875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:04.351089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:07.235027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:10.049581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:12.749853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:15.341241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:18.002709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:20.660001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:24.183802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:26.888019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:29.627114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:32.997992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:35.700127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:38.640061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:41.392765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:44.351381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:47.289352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:50.097964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:54.060929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:57.462233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:01.617317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:04.487041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:07.395460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:10.171615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:12.884124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:15.467105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:18.145486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:20.789192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:24.331563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:27.021874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:29.751582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:33.113077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:35.825741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:38.769022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:41.522816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:44.524251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:47.414652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:50.232577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:54.216298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:57.638818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:01.768940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:04.615648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:07.563653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:10.309181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:13.010742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:15.596112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:18.281830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:20.911045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:24.466619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:27.147269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:29.887673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:33.267641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:35.955110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:38.930604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:41.656102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:44.674456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:47.553128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:50.368735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:54.377641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:57.811881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:01.920246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:04.755920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:07.744666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:10.455698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:13.144937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:15.734728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:18.421926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:21.042388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:24.613107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:27.283623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:30.012342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:33.402998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:36.080170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:39.080067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:41.784236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:44.822200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:47.684897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:50.507310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:54.525118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:58.148089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:02.072383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:04.897295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:07.899351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:10.580512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:13.274590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:15.852332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:18.552324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:21.163662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:24.749255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:27.403351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:30.149002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:33.548354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:36.215891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:39.235116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:41.918884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:44.987172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:47.829326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:50.651117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:54.716560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:58.528208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:02.229995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:05.042389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:08.054670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:10.717108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:13.411958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:15.988617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:18.699609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:21.298902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:24.902199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:27.541628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:30.278254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:33.686928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:36.346136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:39.376769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:42.056324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:45.157515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:47.959893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:50.844506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:54.857491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:08:58.803523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:02.381700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:05.184840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:08.200035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:10.842974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:13.544911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:16.111244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:18.831497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:21.422458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:25.038934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-04-12T01:09:27.666190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2023-04-12T01:09:39.136472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
IndexNbr_CountHello_CntAODV_Msg_Nbr_CntRREQs_Sent_To_NbrRREQs_From_NbrNbr_Is_Orig_CntAll_RREPs_Rcvd_This_NodeRREPs_From_NbrRREP_Resp_PctPct_Of_All_RREPsHop_Cnt_Over_1_CntHop_Cnt_Over_1_PctHigh_Dest_Seq_Num_Inc_CntAvg_Resp_DlyAvg_Resp_Dly_Per_HopRERRs_From_NbrRERRs_From_Nbr_PctPct_of_All_NbrsRREP_To_Nbrs_RatioNodeNbr_NodeNbr_Never_OrigHigh_Dest_Seq_Num_Inc_PctBlack_Hole_Node
Index1.0000.132-0.002-0.0130.151-0.035-0.0010.2350.011-0.002-0.0370.012-0.080-0.0180.0830.161-0.020-0.123-0.132-0.0270.8590.0000.1000.0470.000
Nbr_Count0.1321.000-0.075-0.0360.245-0.052-0.0210.4930.005-0.014-0.0560.0140.003-0.0280.0570.015-0.017-0.084-1.000-0.0150.9950.0000.0000.0000.000
Hello_Cnt-0.002-0.0751.0000.2860.0110.2350.168-0.0380.2320.2270.2480.182-0.0640.1210.0590.0630.177-0.0720.0750.2460.0000.1670.1590.0000.143
AODV_Msg_Nbr_Cnt-0.013-0.0360.2861.0000.0330.9090.5810.0990.8750.8660.8640.846-0.0620.1220.2360.3030.8730.0440.0360.8660.0840.1660.5500.2480.326
RREQs_Sent_To_Nbr0.1510.2450.0110.0331.0000.015-0.0500.249-0.019-0.068-0.057-0.0260.1120.0540.199-0.049-0.075-0.005-0.245-0.0430.9930.0000.1200.0920.000
RREQs_From_Nbr-0.035-0.0520.2350.9090.0151.0000.6060.0630.7170.7090.7200.8620.092-0.1760.1190.1940.9050.2810.0520.7200.0000.1670.5270.3380.000
Nbr_Is_Orig_Cnt-0.001-0.0210.1680.581-0.0500.6061.0000.0830.4960.4940.4940.575-0.028-0.067-0.0360.1210.6100.1870.0210.4950.1080.3220.4360.2290.000
All_RREPs_Rcvd_This_Node0.2350.493-0.0380.0990.2490.0630.0831.0000.1890.1720.0570.2260.114-0.0410.2260.2400.143-0.206-0.4930.0760.9960.0000.1390.0440.000
RREPs_From_Nbr0.0110.0050.2320.875-0.0190.7170.4960.1891.0000.9970.9720.825-0.2120.3530.3460.4410.772-0.445-0.0050.9760.0340.3670.4540.5370.964
RREP_Resp_Pct-0.002-0.0140.2270.866-0.0680.7090.4940.1720.9971.0000.9780.819-0.2310.3530.3090.4350.767-0.4560.0140.9790.0000.3970.5350.5550.992
Pct_Of_All_RREPs-0.037-0.0560.2480.864-0.0570.7200.4940.0570.9720.9781.0000.786-0.2930.3650.1920.3140.759-0.3490.0560.9980.1210.2200.2860.5340.939
Hop_Cnt_Over_1_Cnt0.0120.0140.1820.846-0.0260.8620.5750.2260.8250.8190.7861.0000.363-0.1810.4260.4390.914-0.056-0.0140.7900.0340.2010.5090.0000.000
Hop_Cnt_Over_1_Pct-0.0800.003-0.064-0.0620.1120.092-0.0280.114-0.212-0.231-0.2930.3631.000-0.4410.406-0.0230.1330.148-0.003-0.2950.0880.3530.3230.4930.776
High_Dest_Seq_Num_Inc_Cnt-0.018-0.0280.1210.1220.054-0.176-0.067-0.0410.3530.3530.365-0.181-0.4411.000-0.008-0.058-0.171-0.3750.0280.3660.0800.4840.0880.5690.997
Avg_Resp_Dly0.0830.0570.0590.2360.1990.119-0.0360.2260.3460.3090.1920.4260.406-0.0081.0000.6520.148-0.254-0.0570.2170.1000.2170.1840.0000.000
Avg_Resp_Dly_Per_Hop0.1610.0150.0630.303-0.0490.1940.1210.2400.4410.4350.3140.439-0.023-0.0580.6521.0000.264-0.306-0.0150.3410.1400.3160.2290.0000.000
RERRs_From_Nbr-0.020-0.0170.1770.873-0.0750.9050.6100.1430.7720.7670.7590.9140.133-0.1710.1480.2641.0000.4340.0170.7600.0310.1830.5880.1590.031
RERRs_From_Nbr_Pct-0.123-0.084-0.0720.044-0.0050.2810.187-0.206-0.445-0.456-0.349-0.0560.148-0.375-0.254-0.3060.4341.0000.084-0.3670.0970.2500.0000.2000.000
Pct_of_All_Nbrs-0.132-1.0000.0750.036-0.2450.0520.021-0.493-0.0050.0140.056-0.014-0.0030.028-0.057-0.0150.0170.0841.0000.0150.9950.0000.0000.0180.000
RREP_To_Nbrs_Ratio-0.027-0.0150.2460.866-0.0430.7200.4950.0760.9760.9790.9980.790-0.2950.3660.2170.3410.760-0.3670.0151.0000.0770.2840.2320.5260.911
Node0.8590.9950.0000.0840.9930.0000.1080.9960.0340.0000.1210.0340.0880.0800.1000.1400.0310.0970.9950.0771.0000.0000.1180.0000.000
Nbr_Node0.0000.0000.1670.1660.0000.1670.3220.0000.3670.3970.2200.2010.3530.4840.2170.3160.1830.2500.0000.2840.0001.0000.3970.5170.934
Nbr_Never_Orig0.1000.0000.1590.5500.1200.5270.4360.1390.4540.5350.2860.5090.3230.0880.1840.2290.5880.0000.0000.2320.1180.3971.0000.2400.098
High_Dest_Seq_Num_Inc_Pct0.0470.0000.0000.2480.0920.3380.2290.0440.5370.5550.5340.0000.4930.5690.0000.0000.1590.2000.0180.5260.0000.5170.2401.0000.995
Black_Hole_Node0.0000.0000.1430.3260.0000.0000.0000.0000.9640.9920.9390.0000.7760.9970.0000.0000.0310.0000.0000.9110.0000.9340.0980.9951.000

Missing values

2023-04-12T01:09:30.522773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-12T01:09:31.587128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-12T01:09:31.891172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IndexNodeNbr_NodeNbr_CountHello_CntAODV_Msg_Nbr_CntRREQs_Sent_To_NbrRREQs_From_NbrNbr_Is_Orig_CntNbr_Never_OrigNbr_Is_Dest_CntNbr_Never_DestAll_RREPs_Rcvd_This_NodeRREPs_From_NbrRREP_Resp_PctPct_Of_All_RREPsHop_Cnt_Over_1_CntHop_Cnt_Over_1_PctHigh_Dest_Seq_Num_Inc_CntHigh_Dest_Seq_Num_Inc_PctAvg_Resp_DlyAvg_Resp_Dly_Per_HopRERRs_From_NbrRERRs_From_Nbr_PctPct_of_All_NbrsRREP_To_Nbrs_RatioBlack_Hole_Node
0110.1.1.4010.1.1.37371246261371False0True73351.920.6800005100.0000.04.7432250.458547480.002.70.251852False
1210.1.1.4010.1.1.33719026100True0True73300.000.0000000NaN0NaNNaNNaN0NaN2.70.000000False
2310.1.1.4010.1.1.5374134261180True0True733145.361.91000014100.0000.03.9864300.619930214.292.70.707407False
3410.1.1.4010.1.1.12376217126113834False0True733186.902.4600001794.4400.03.6059550.5888451583.332.70.911111False
4510.1.1.4010.1.1.41371334332612391False0True73311242.9115.2800008979.4600.02.6204960.4121128273.212.75.659259False
5610.1.1.4010.1.1.50374172261380True0True733228.433.00000022100.0000.03.5173270.4681471254.552.71.111111False
6710.1.1.4010.1.1.3437944261330True0True73351.920.6800005100.0000.02.6558530.3945686120.002.70.251852False
7810.1.1.4010.1.1.47377026100True0True73300.000.0000000NaN0NaNNaNNaN0NaN2.70.000000False
8910.1.1.4010.1.1.23718120261592False0True7333814.565.1800002565.7900.01.7648270.3628622360.532.71.918519False
91010.1.1.4010.1.1.45372626526100True0True733261100.0035.60709400.00261100.03.2365830.58236500.002.713.187813True
IndexNodeNbr_NodeNbr_CountHello_CntAODV_Msg_Nbr_CntRREQs_Sent_To_NbrRREQs_From_NbrNbr_Is_Orig_CntNbr_Never_OrigNbr_Is_Dest_CntNbr_Never_DestAll_RREPs_Rcvd_This_NodeRREPs_From_NbrRREP_Resp_PctPct_Of_All_RREPsHop_Cnt_Over_1_CntHop_Cnt_Over_1_PctHigh_Dest_Seq_Num_Inc_CntHigh_Dest_Seq_Num_Inc_PctAvg_Resp_DlyAvg_Resp_Dly_Per_HopRERRs_From_NbrRERRs_From_Nbr_PctPct_of_All_NbrsRREP_To_Nbrs_RatioBlack_Hole_Node
36836910.1.1.4910.1.1.193512025400True0True43000.000.0000000NaN0NaNNaNNaN0NaN2.860.000000False
36937010.1.1.4910.1.1.44359025400True0True43000.000.0000000NaN0NaNNaNNaN0NaN2.860.000000False
37037110.1.1.4910.1.1.49354424025400True0True430254100.0059.06976700.00254100.02.6479150.47231200.02.8620.653765True
37137210.1.1.4910.1.1.63511025400True0True43000.000.0000000NaN0NaNNaNNaN0NaN2.860.000000False
37237310.1.1.4910.1.1.36355025400True0True43000.000.0000000NaN0NaNNaNNaN0NaN2.860.000000False
37337410.1.1.4910.1.1.7351850254460True0True43041.570.930000125.0000.00.1214960.03287400.02.860.325175False
37437510.1.1.4910.1.1.32352025400True0True43000.000.0000000NaN0NaNNaNNaN0NaN2.860.000000False
37537610.1.1.4910.1.1.2835311392541040True0True430145.513.260000428.5700.01.8049360.40632621150.02.861.139860False
37637710.1.1.4910.1.1.1635221272547233False0True430218.274.8800001676.1900.02.0438780.43198034161.92.861.706294False
37737810.1.1.4910.1.1.4635713254120True0True43010.390.2300001100.0000.03.3354820.47649700.02.860.080420False